What Would it Take to Train an Agent to Play with a Shape-Sorter?
The capabilities of humans to precisely and robustly recognise and manipulate objects has been instrumental in the development of human cognition. However, understanding and replicating this process has proven to be difficult. This is of particular importance when thinking of agents or robots acting in naturalistic environments, solving complex tasks. I will present recent work in this direction, focusing on computational optimality and Deep Reinforcement Learning techniques, to discover how to manipulate objects within a 3D physics simulator from high-dimensional sensory observations.
Feryal has received her PhD from the Department of Computing at Imperial College London where she studied Computational Neuroscience and Machine Learning at the Brain and Behaviour Lab. Her main research focused on investigating the underlying algorithms employed by the human brain for object representation and inference. She has previously obtained her MSc in Artificial Intelligence with distinction at Imperial College London. She has also worked on projects building machine learning solutions as part of a technology consultancy start-up that she co-founded. Currently, she is a visiting postdoctoral researcher at Imperial College London where she works on transfer learning and deep reinforcement learning.